Intent based second pass ranker for ranking aggregates

ABSTRACT

Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user&#39;s intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.

TECHNICAL FIELD

The present disclosure relates to machine learned optimization forranking groups of content items for presentation on a client device.

BACKGROUND

Content management systems are designed to provide content items tousers for consumption. Content items may represent content such asphotos, videos, job posts, news articles, other users to connect with,documents, user posts, audio, and many more. Content items may begrouped together in groups, referred to as cohorts. The grouping ofcontent items within cohorts may be based upon common features ofgrouped content items. For example, if a cohort contains suggested usersthat a target user may want to connect with, then the cohort may bedefined by common features of the suggested users such as, each of thesuggested users work at the same company as the target user or each ofthe suggested users attended the same school as the target user. Inanother example, content items related to a specific topic may begrouped together to form a cohort defined by the specific topic.Different cohorts of content items may be presented to the target userwithin a user session for the purpose of causing user-engagement by thetarget user. Cohorts provide the target user with an explanation as towhy groups of content items are being presented to the target user.

Conventional content management systems that implement grouping contentitems into cohorts often present the cohorts in a predefined format. Forexample, content items within the “people you may know” cohort may bepresented first followed by a “subject matter you may be interested in”cohort, and so on. However, predefining the order for presenting cohortmay be effective for increasing user engagement when users' intentionsalign with the predetermined order of the cohorts. For example, if auser is interested in growing his/her network, then presenting the“people you may know” cohort first may result in increased engagement.However, if a second user intends to consume content about the topic ofmachine learning, then presenting cohorts in a predetermined order,where the “people you may know” cohort is presented first and thesubject matter about machine learning cohort is presented third orfourth, is unlikely to increase user engagement of the second userbecause the cohort containing content items related to machine learningis presented far below other cohorts of content items not related to themain intention of the user.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a block diagram that depicts a system for selecting anddistributing content items to one or more end-users, in an embodiment.

FIG. 2 illustrates an example of how ranked cohorts may be displayed toa user on a client device, in an embodiment.

FIG. 3 depicts an example flowchart for scoring and ranking a pluralityof cohorts, and causing display of a plurality of content items withintheir respective ranked cohorts on a client device, in an embodiment.

FIG. 4 depicts an example flowchart for training a cross-cohortoptimization model by determining regression coefficients and latentintent values, in an embodiment.

FIG. 5 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

General Overview

In an embodiment, a content management system performs content itemselection events that involve selecting content items to present to aclient device. The content items may be grouped into cohorts forpresentation on a client device. A cohort may represent a group ofcontent items grouped together based upon one or more common attributes.For example, content items that represent articles on artificialintelligence may be grouped into an “articles related to artificialintelligence” cohort. In another example, content items representingother users who attended University X may be grouped together to form“people who attended University X” cohort.

In an embodiment, the selected content items are presented within thecohorts, where the cohorts are ranked based upon their respectiverelevance to a particular user's intentions. The content managementsystem may implement a cross-cohort optimization model that optimizes apresentation order for the cohorts by ranking each of the cohorts withrespect to the particular user's intentions. For example, if theparticular user's intention is to increase their network of connectedusers, then the cross-cohort optimization model may rank the cohortssuch that cohorts containing content items aimed at increasing theparticular user's network of connected users are ranked higher thanother cohorts. Cohorts with higher rankings may be presented to theparticular user first, in order to facilitate user engagement with thecontent items that are most relevant to the particular user'sintentions.

In an embodiment, a cross-cohort optimization system may store, withinmemory, cohort definition criteria for each cohort of the plurality ofcohorts. For a particular user, for each cohort, the cross-cohortoptimization system may identify content items that belong to a specificcohort based upon the cohort definition criteria. Using amachine-learned model, the cross-cohort optimization system may generatea score for the specific cohort with respect to the particular user'sintentions. The cross-cohort optimization system may generate a rankingfor the cohorts based on the respective scores of each cohort. Based onthe ranking of the cohorts, the content management system may cause thecontent items of each cohort to be displayed concurrently on a computingdevice of the particular user.

The disclosed approaches provide advantages over conventional solutionsby implementing a cross-cohort optimization model that scores and rankscohorts of content items based upon known and unknown intentions of theparticular user. The dynamic selection and ranking of cohorts on anindividual user basis ensures that relevant content will be presented toeach user of the system. By optimizing which categories of content itemsare presented to each user's landing page, user engagement may beincreased over conventional solutions that statically configure wherecategories of content items are presented on a user's landing page.

System Overview

FIG. 1 is a block diagram that depicts a system 100 for selecting anddistributing content items to one or more end-users, in an embodiment.System 100 may represent a least some portions of a content managementsystem. System 100 includes a cross-cohort optimization system 105, acontent delivery system 120, a data store 130, a publisher system 140,and client devices 142-146. Content providers (not shown) providecontent items to content delivery system 120, which in turn selectscontent items to provide to publisher system 140 for presentation tousers of client devices 142-146. Content providers communicate withcontent delivery system 120 over a network, such as a LAN, WAN, or theInternet.

An example of a content provider includes an advertiser. An advertiserof a product or service may be the same party as the party that makes orprovides the product or service. Alternatively, an advertiser maycontract with a producer or service provider to market or advertise aproduct or service provided by the producer/service provider. Anotherexample of a content provider is an online ad network that contractswith multiple advertisers to provide content items (e.g.,advertisements) to end users, either through publishers directly orindirectly through content delivery system 120.

Although depicted in a single element, content delivery system 120 maycomprise multiple computing elements and devices, connected in a localnetwork or distributed regionally or globally across many networks, suchas the Internet. Thus, content delivery system 120 may comprise multiplecomputing elements, including file servers and database systems. Forexample, content delivery system 120 may include a content providerinterface that allows content providers to create and manage theirrespective content delivery campaigns and a content delivery exchangethat conducts content item selection events in response to contentrequests from a third-party content delivery exchange and/or frompublisher systems, such as publisher system 140.

Publisher system 140 provides its own content to client devices 142-146in response to requests initiated by users of client devices 142-146.The content may be about any topic, such as news, sports, finance, andtraveling. Publishers may vary greatly in size and influence, such asFortune 500 companies, social network providers, and individualbloggers. A content request from a client device may be in the form of aHTTP request that includes a Uniform Resource Locator (URL) and may beissued from a web browser or a software application that is configuredto only communicate with publisher system 140 (and/or its affiliates). Acontent request may be a request that is immediately preceded by userinput (e.g., selecting a hyperlink on web page) or may be initiated aspart of a subscription, such as through a Rich Site Summary (RSS) feed.In response to a request for content from a client device, publishersystem 140 provides the requested content (e.g., a web page) to theclient device.

Simultaneously or immediately before or after the requested content issent to a client device, a content request is sent to content deliverysystem 120. That request is sent (over a network, such as a LAN, WAN, orthe Internet) by publisher system 140 or by the client device thatrequested the original content from publisher system 140. In response toreceiving a content request the content delivery system 120 initiates acontent item selection event that involves selecting one or more contentitems (from among multiple content items) to present to the clientdevice that initiated the content request. An example of a content itemselection event is an auction.

Content delivery system 120 and publisher system 140 may be owned andoperated by the same entity or party. Alternatively, content deliverysystem 120 and publisher system 140 are owned and operated by differententities or parties.

A content item may comprise an image, a video, audio, text, graphics,virtual reality, or any combination thereof. A content item may alsoinclude a link (or URL) such that, when a user selects (e.g., with afinger on a touchscreen or with a cursor of a mouse device) the contentitem, a (e.g., HTTP) request is sent over a network (e.g., the Internet)to a destination indicated by the link. In response, content of a webpage corresponding to the link may be displayed on the user's clientdevice. A content item may also represent a user profile of another userof the content management system. For instance, when a user selects thecontent item representing another user's profile, a request is sent tothe content management system to display the user profile page for theselected user.

Examples of client devices 142-146 include desktop computers, laptopcomputers, tablet computers, wearable devices, video game consoles, andsmartphones.

In an embodiment, the data store 130 may represent data storageconfigured to store user interaction data from user sessions, userprofile data, such as user attributes, content item selection data, suchas marketplace auction data, cohort definition criteria that definesassociated attributes of cohorts. For example, the data store 130 maystore user profile data, a weeks' worth of user interaction data,content item attribute data used to group content items within cohorts,as well as cohort definition criteria.

Cohorts

As described, cohorts are groups of content items that share one or moresimilar characteristics, such as similar user profile attributes, orsimilar content item properties. Cohorts may be categorized by the typeof content items contained within the cohort and/or by the similaritiesbetween content item properties. For example, content items thatrepresent other user profiles may by grouped within“people-you-may-know” cohort types. The people-you-may-know cohort typemay include several different cohorts that may be based on common userprofile attributes of other users. For instance, the people-you-may-knowcohort type may include, but is not limited to, cohorts based upon acommon educational institution, common employer, common interests,common social groups, common geographic area, and any other relevantuser profile attribute. For example, if a target user's profileindicates that they attended UC Berkeley, then a “people-you-may-knowwho attended UC Berkeley” cohort that contains content items of userprofiles of users that attended UC Berkeley may be relevant to thetarget user. In another example, if the target user works at LinkedIn,then a “people-you-may-know who work at LinkedIn” cohort may be relevantto the target user.

Other cohort types may include cohorts based on a particular topic ofinterest, cohorts related to a specific series, and any other propertyor attribute that may be used to group content items together. Anexample of generating cohorts based on a particular topic may includehashtag cohorts which group content items together that have been taggedusing the same hashtag. For instance, an artificial intelligence cohortmay include content items that have been tagged with one or morehashtags related to artificial intelligence.

In an embodiment, a plurality of cohorts may be ranked for presentationbased upon their relevance to the target user, where the target userrepresents the user engaged in a particular user session. For example,if the target user initiates a user session to find other users for thepurpose of growing their network of connections, then the plurality ofcohorts may be ranked in an order that displays cohorts related toconnecting to other users first. By displaying the most relevant cohortsfirst, the target user may be able to more efficiently find other usersof interest and connect to the other users.

FIG. 2 illustrates an example of how ranked cohorts may be displayed toa user on a client device 142. User 250 represents the target user.Cohort rankings for the plurality of cohorts may differ depending on theidentified intentions of user 250. For example, if user 250's intentionfor their corresponding user session is to increase their network ofusers, then the cohort ranking may rank cohorts that include userprofiles higher than other types of cohorts. Cohort people-you-may-knowA (PYMK-A 200) may represent user profiles of users that attended thesame university as user 250. Cohort PYMK-B 210 may represent userprofiles of users that work in the same industry as user 250. Cohorthashtag 220 may represent content items related to a particular topic,such as artificial intelligence. Cohort series 230 may represent a setof video content items for a specific series on learning how designmobile applications. Cohorts 200 and 210 are ranked higher than cohorts220 and 230 because cohorts 200 and 210 contain content items of userprofiles, which is more relevant to user 250's intention of increasingtheir network of users. If, however, user 250's intention is to consumecontent related to artificial intelligence, then cohort 220 may beranked higher than the other cohorts.

Cross-Cohort Optimization System

In an embodiment, the cross-cohort optimization system 105 implements across-cohort optimization model that generates a user-specific score foreach cohort based upon the user's derived user intentions for their usersession. A user may have one or more specific intentions in mind wheninitiating a user session. For example, a user may intend to, duringtheir user session, increase their network of users, consume contentabout a particular topic, and/or any other desired intention. Users arenot limited to having only one specific intention in mind and may havemultiple intentions for a user session. User intentions may be derivedfrom a user's prior interactions in previous user sessions, a user'sprofile attributes, and any other observable user metrics.

The cross-cohort optimization model may use a set of identified userintentions, such as increasing the network of users or consuming contenton a specific subject. However, there may be additional user intentions,not previously known, that may factor into the cross-cohort optimizationmodel. In an embodiment, the cross-cohort optimization model maydiscover one or more latent intentions of the user. Latent intentionsmay represent user intentions that are not directly observable but areinferred from the cross-cohort optimization model itself. The known userintentions and the derived latent intentions may be used by thecross-cohort optimization model to score each cohort with respect to thespecific user.

In an embodiment, the calculated score for a particular cohort from thecross-cohort optimization model may represent how likely content itemswithin the particular cohort satisfy the intentions of the user. A highscore calculated for the particular cohort may increase the likelihoodthat the user will click on or otherwise interact with a content itemwithin the particular cohort. For example, if the user's intentionsinclude consuming content related to machine learning, then theparticular cohort representing “articles on machine learning” mayreceive a high score from the cross-cohort optimization model, as it ismore likely that the user will interact with content items within an“articles on machine learning” cohort than content items within othercohorts. A low score calculated for a second cohort may indicate that itis less likely that the user will interact with content items within thesecond cohort. For instance, if the second cohort represents“people-you-may-know that attended UC Berkeley” and the user'sintentions include consuming content related to machine learning, thenthe second cohort may receive a lower score than other cohorts relatedto machine learning content.

In an embodiment, the cross-cohort optimization system 105 may becommunicatively coupled to the content delivery system 120 for thepurposes of ranking a plurality of cohorts that contain content itemsselected by the content delivery system 120 for a content deliverycampaign. In another embodiment (not shown in FIG. 1), the cross-cohortoptimization system 105 may be integrated as part of the contentdelivery system 120. In an embodiment, the cross-cohort optimizationsystem 105 may include a cross-cohort optimization model generationservice 110 and a cross-cohort optimization model scoring service 115.

In an embodiment, the cross-cohort optimization system 105 may becommunicatively coupled to the data store 130 for the purposes ofretrieving user-specific profile attributes, user interaction data,cohort attribute data, content item specific data, and storing cohortdefinition criteria as well as user and cohort specific model parametervalues and user-specific cohort scores.

Cross-Cohort Optimization Model

The cross-cohort optimization model may be implemented using analgorithm that incorporates user and cohort features, identified userintentions, latent intentions, utility scores representing contentmanagement system objectives, and error and bias terms. In anembodiment, the cross-cohort optimization model may be implemented usinga logistic regression algorithm with gradient descent. The cross-cohortoptimization model may be represented as:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {l_{i}^{T}c_{j}} + {\gamma P_{ij}^{FPR}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}} + ɛ_{ij}}$

where:

i represents the index value for a particular user and j represents theindex value for a particular cohort.

The logit(y_(ij)) function is a natural log of the probability thaty_(ij) equals 1, where y_(ij)=1, if the user i clicks on one of the top3 presented content items within cohort j. The top 3 presented contentitems represent content items, within the cohort, that are presented atthe client device without needing the user to scroll within the cohortto click on a specific content item. For example, cohorts may containseveral content items which may be viewed by scrolling horizontallywithin the cohort j. The top 3 content items may be initially presentedto the user, within the cohort, such that the user does not need toscroll in order to click on one of the top 3 content items. The numberof top content items may be configurable depending on the layout of thecontent items and the size of the display area within the client device.For instance, if the user is using a desktop computer for the usersession, then the cohort may be able to initially display 5 contentitems within the cohort without having to scroll through the contentitems.

x_(ij) represents a feature vector that contains user features andcohort features, where the user and cohort features represent attributesand other observed metrics, such as user interaction data.

The s_(i,connect) and s_(i,content) represent observable user intents.The s_(i,connect) represents an affinity score reflecting the intent ofuser i to engage with PYMK cohort types. The s_(i,content) represents anaffinity score reflecting the intent of user i to engage withcontent-based cohort types. In an embodiment, the cross-cohortoptimization model may include more or less affinity scores representingother cohort types.

The l_(i) ^(T)c_(j) values represent the latent factors which captureunobservable latent intents of user i. The l_(i) ^(T) represents atranspose of a vector containing the latent intents of user i. The c_(j)represents a vector containing values that represent the propensity ofusers to interact with cohort j with respect to the latent intents. Forexample, if three latent intents are derived then l_(i) ^(T) transposevector may be represented as [l_(i1), l_(i2), l_(i3)]_(1×3) for thethree latent intents, and the c_(j) vector may be represented as[c_(j1), c_(j2), c₃]_(1×3) for the three latent intents.

The P_(ij) ^(FPR) represents a value calculated by a first-pass rankeralgorithm for recommending content items for a particular user using aseparate content item selection model implemented by the contentdelivery system 120.

The Σ_(k) δ_(k)1_({Type of j==k}) represents a bias term for previouslyundefined cohort types. The 1_({Type of j==k}) is an indicator featurethat indicates whether cohort j is of cohort type k, where cohort type kis an element of the set of defined cohort types. For example, definedcohort types may include the set of {PYMK, hashtag, series, group}. Theδ_(k) represents an unknown configurable parameter that is determinedduring model training.

The ε_(ij) is an error term.

The β, α₁, α₂, γ, δ_(k) represent unknown logistic regressioncoefficients that are determined during model training.

Training the cross-cohort optimization model is described in theCROSS-COHORT OPTIMIZATION MODEL TRAINING section herein.

User & Cohort Features

In an embodiment, the feature vector x_(ij) may contain severaldifferent user-based features, cohort-based features, and user-cohortpair features. User-based features may include, but are not limited to,user profile attributes, user interaction features, user invitationbased features, user network features, and job seeking intent features.User profile attributes include attributes about the user that arestored within a user's profile, such as current and past employer,educational degrees, educational institutions attended, social groups,educational and professional certificates, and past and present place ofresidence, and any other recorded profile attribute.

User interaction features may represent behavior of how the userinteracted with various tabs, pages, and products within the contentmanagement system. For example, the user interaction features mayinclude the user's historical interaction activity with the PYMK tab orwith any other page or product within the content management system. Theuser interaction features may be collected and analyzed by other machinelearning models implemented by the content delivery system 120 or othersystems within the larger content management system.

User invitation based features may represent metrics for how manyinvitations to connect a user received over a specific period of time.For example, the user received 100 invitations to connect from otherusers. In an embodiment, the user invitation based features may berepresented as the log(1+(number of invitations received)). As anotherexample of a user invitation based feature, the user has sent 50invitations to connect with other users.

User network features may represent metrics representing the number ofconnections a user has. For example, if a user's network of first-degreeconnections is 400 people, then their user network would be 400 otherusers. In an embodiment, the user network features may be represented asthe log(1+connection_count).

The job seeking intent features may represent observed attributes anduser interactions that relate to the job seeking intent. For example,user interactions where the user visits the PYMK tab, interacts with thejob search features and views job search results, visits pages of usersand companies that are actively looking for candidates, and any otherobservable job seeking interactions. In an embodiment, the cross-cohortoptimization model may derive a job seeking intent feature value fromthe observed user attributes and user interactions. In anotherembodiment, a separate machine-learned model implemented by either thecontent delivery system 120 or another system may be used to derive thejob seeking intent feature value.

In an embodiment, user-cohort pair features may include, but are notlimited to, user-cohort specific impression metrics, the degree ofrelationships between users, and any other user/cohort commonattributes. The user-cohort specific impression metrics may representimpression metrics that are specific to the user and the specificcohort. For example, impression count metrics may be identified forimpressions of content items within the specific cohort for the specificuser. The degree of relationships between users may represent how manycommon connections exist between a pair of users and/or the number ofcommon connections between the specific user and content items thatrepresent other users within a specific cohort. For example, user A maybe connected to users D and E and user B may be connected to users C andD. User A and user B have a common connection of user D, and as a resultthe common connection value between users A and B would be 1. If anotheruser, such as user Y, has no common connections with user A, then thecommon connection value between users A and Y would be zero.

In an embodiment, cohort-based features may represent cohort attributessuch as the type of cohort, content item-based rankings, random-effectcoefficients, and any other cohort attributes. An example of the type ofcohort attribute may be PYMK for a cohort that represents“people-you-may-know who work at LinkedIn”. Another example may be acontent cohort type for the cohort “articles related to AI.”

The random-effect-coefficients may represent a multi-dimensional vector,such as a 5-dimensional vector, calculated using a machine-learnedmodel. The machine-learned model may be any commercial or proprietarymodel that is used to calculate a random-effect-coefficient-embeddingfor each cohort. The random-effect-coefficient-embeddings capturesimilarities between cohorts and are a mathematical representation ofthe cohorts in a 5-dimensional space, such that if two cohorts aresimilar then the dot-product between the 5-dimensionrandom-effect-coefficient-embeddings of the two cohorts would be a largevalue. The random-effect-coefficient-embedding is used as a cohortfeature such that, for cohort j, the embedding is concatenated as a5-dimensional vector with the x_(ij) feature vector in the cross-cohortoptimization model.

First-Pass Ranker Score

The P_(ij) ^(FPR) represents a value calculated by a first-pass rankeralgorithm for recommending content items for a particular user. In anembodiment, the first-pass ranker may represent an externalmachine-learned model implemented to score and rank content items forpresentation to a user. The first-pass ranker (FPR) score may be basedon a desired objective such as optimizing for click-throughs. Someembodiments of the FPR score may represent any other desired objectivesuch as, optimizing for increasing a network of connections(P_(connect)), increasing the number of hashtags followed by a user(P_(follow)), increasing the number of subscriptions to content(P_(subscribe)), or any other objective. In yet other embodiments, FPRscores for different objectives may be merged to represent multipledesired objectives.

The content delivery system 120 may implement the first-pass rankeralgorithm, which may by any conventional or proprietary machine-learnedmodel. In an embodiment, a set of content items may be assigned an FPRscore by the content delivery system 120. The set of content items maybe ranked, and then a subset of the top 5 content items may be selectedfor calculating the P_(ij) ^(FPR) value for the cohort that contains theset of content items. The P_(ij) ^(FPR) value may be calculated as anaverage of the FPR scores of the top 5 content items in the subset:

P _(ij) ^(FPR)=⅕Σ_(h=1) ⁵ P _(ijh) ^(FPR)

where P_(ijh) ^(FPR) represents the FPR score for user i and contentitem h within cohort j.

In other embodiments, the number of content items used to calculate thecohort P_(ij) ^(FPR) may vary. For example, the P_(ij) ^(FPR) value maybe an average of 3 content item FPR scores or may be an average of 10content item FPR scores. In yet other embodiments, the positional valueof each content item within the cohort may be used to assign a weight toeach FPR score for each content item. For instance, the content item inthe first position may be given a higher weight than the content item inthe fifth position. In yet other embodiments, the P_(ij) ^(FPR) valuemay be implemented using other functions other than an average function,such as median, or weighted averages.

Affinity Scores for Observable User Intents

The calculated affinity scores represent scores assigned to observableintents for the user. For example, s_(i,connect) represents an affinityscore reflecting the intent of user i to engage with PYMK cohort types.In an embodiment, the s_(i,connect) may be calculated as the maximumlikelihood estimation (MLE) of the number of clicks on a content itemwithin a PYMK cohort divided by the total number of clicks by the user.Calculating the s_(i,connect) may be represented as:

$s_{i,{{conne}ct}} = \frac{\# \left( {y_{i,{{{PYM}K} - {cohort}}} = 1} \right)}{{\Sigma_{j}\# \left( {y_{i,j} = 1} \right)} + 2 + {C} + 1}$

where the numerator (i.e., #(y_(i,PYMK-cohort)=1)) represents the numberof clicks on content items within cohorts that have a PYMK type. Thedenominator (i.e., Σ_(j) #(y_(i,j)=1)+2+|C|+1) is the sum of the totalnumber of clicks on content items within all cohorts, where |C| is thetotal number of identified cohorts.

In an embodiment, the s_(i,content) may be calculated as the MLE of thenumber of clicks on a content item within a content cohort divided bythe total number of clicks by the user. Calculating the s_(i,content)may be represented as:

$s_{i,{{conte}nt}} = \frac{\# \left( {y_{i,{{Content} - {cohort}}} = 1} \right)}{{\Sigma_{j}\# \left( {y_{i,j} = 1} \right)} + 2 + {C} + 1}$

where #(y_(i,Content-cohort)=1) represents the number of clicks oncontent items within cohorts that have a content type. The Σ_(j)#(y_(i,j)=1)+2+|C|+1 is the sum of the total number of clicks on contentitems within all cohorts, where |C| is the total number of identifiedcohorts.

FPR-Independent Cross-Cohort Optimization Model

In some scenarios, the cross-cohort optimization model may need to bere-trained when FPR scores, for the plurality of cohorts, are notavailable or new FPR scores have been calculated by the content deliverysystem 120. If new FPR scores have been calculated, then each of theregression coefficients and the latent intent values will need to berecalibrated. In an embodiment, the cross-cohort optimization model maybe implemented using a logistic regression algorithm with gradientdescent that is not dependent on FPR scores. This embodiment of thecross-cohort optimization model may be represented as:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {l_{i}^{T}c_{j}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}} + ɛ_{ij}}$

where the cohort γP_(ij) ^(FPR) has been omitted.

Additional Cross-Cohort Optimization Model Embodiments

In an embodiment, the cross-cohort optimization model may be implementedwhere the FPR score component is factored into the model at aper-content item (per cohort) basis. That is, the P_(ij) ^(FPR) score isfactored for each content item. The cross-cohort optimization model maybe represented as:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {l_{i}^{T}c_{j}} + {\gamma_{j}P_{ij}^{FPR}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}} + ɛ_{ij}}$

where the γ_(j) represents the item (cohort) level effects for theP_(ij) ^(FPR) FPR scores.

In another embodiment, the cross-cohort optimization model may beimplemented where the latent member intents are determined using latentprojection of user features. The cross-cohort optimization model may berepresented as:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {\delta_{j}{g\left( x_{i} \right)}} + {\gamma_{j}P_{ij}^{FPR}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}} + ɛ_{ij}}$

where:δ_(j) is the content item (cohort) level random effect,g function is a parameterized unknown function,x_(i) is a feature vector containing user features, andg(x_(i)) is a latent projection of user features that represents latentuser intents.

Cross-Cohort Optimization Model Generation Service

The cross-cohort optimization model generation service 110 may storecohort definition criteria in the data store 130. Cohort definitioncriteria may include cohort attributes used to describe specificcohorts. Cohort definition criteria may be derived from user profilesand user interaction data observed by the cross-cohort optimizationsystem 105 or may be provided by content management systemadministrators or other users. For example, an administrator maymanually define cohort attributes for a new cohort. The cross-cohortoptimization model generation service 110 may then store cohortdefinition criteria for the new cohort in the data store 130.

In an embodiment, the cross-cohort optimization model generation service110 may retrieve, from the data store 130, user attributes andhistorical user interaction data for users, cohort definition criteria,including cohort attributes, and any other user, cohort, and contentitem information needed to generate and train the cross-cohortoptimization model for a specific user.

Cross-Cohort Optimization Model Scoring Service

In an embodiment, the cross-cohort optimization model scoring service115 assigns a score to each of the cohorts for the specific user usingthe cross-cohort optimization model. For example, the cross-cohortoptimization model scoring service 115 may calculate scores for eachcohort using the specific user's profile attributes and historical userinteraction data, the cohort attributes, and the identified contentitems for each respective cohort. Upon calculating scores for eachcohort, the cross-cohort optimization model scoring service 115 may rankthe cohorts for presentation on a client device.

Processing Overview

FIG. 3 depicts an example flowchart for scoring and ranking a pluralityof cohorts, and causing display of a plurality of content items withintheir respective ranked cohorts on a client device. Process 300 may beperformed by a single program or multiple programs. The operations ofthe process as shown in FIG. 3 may be implemented usingprocessor-executable instructions that are stored in computer memory.For purposes of providing a clear example, the operations of FIG. 3 aredescribed as performed by the cross-cohort optimization system 105 andits components. For the purposes of clarity process 300 is described interms of a single entity.

In operation 305, process 300 stores cohort definition criteria for eachcohort of a plurality of cohorts. In an embodiment, the cross-cohortoptimization model generation service 110 stores, within the data store130, cohort definition criteria that may include cohort attributes usedto describe the cohorts and to identify content items belonging torespective cohorts. In an embodiment, the cross-cohort optimizationmodel generation service 110 may optionally store newly identifiedcohorts, such as user-defined cohorts.

In an embodiment, for a particular user, the cross-cohort optimizationmodel scoring service 115 may iterate over each cohort of the pluralityof cohorts to calculate scores for each cohort using the cross-cohortoptimization model. Operations 310-320 describe the process of selectingand scoring each cohort of the plurality of cohorts with respect to theparticular user. In operation 310, process 300 determines whether thereare remaining cohorts to score for the particular user. In anembodiment, if there are remaining cohorts to score, then cross-cohortoptimization model scoring service 115 selects the next cohort from theplurality of cohorts that needs to be scored and proceeds to operation315. If, however, there are no remaining cohorts to score, thencross-cohort optimization model scoring service 115 proceeds tooperation 325 to generate a ranking of the plurality of cohorts.

In operation 315, process 300 identifies a plurality of content itemsbased on the cohort definition criteria of said cohort. In anembodiment, the cross-cohort optimization model scoring service 115retrieves from the content delivery system 120 the plurality of contentitems that are to be presented to the particular user. The contentdelivery system 120 may implement any number of content item selectionalgorithms to select the plurality of content items for the particularuser. Upon retrieving the plurality of content items for the particularuser, the cross-cohort optimization model scoring service 115 identifiesa subset of content items that match the cohort definition criteria forthe selected cohort. For example, if the selected cohort is “contentrelated to AI” then the cross-cohort optimization model scoring service115 may identify content items that are related to the topic AI, such asarticles, posts, shares, and any other content that has been tagged withan AI hashtag or is otherwise related to the topic AI.

In operation 320, process 300 uses a machine-learned model to generate ascore for the selected cohort. In an embodiment, the cross-cohortoptimization model scoring service 115 may use cross-cohort optimizationmodel algorithm to score the selected cohort:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {l_{i}^{T}c_{j}} + {\gamma P_{ij}^{FPR}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}} + ɛ_{ij}}$

where i is the particular user and j is the selected cohort.

Upon generating a score for the selected cohort, process 300 proceedsback to operation 310 to determine whether there are any remainingcohorts, in the plurality of cohorts, to score. If there are noremaining cohorts to score, then process 300 proceeds to operation 325.

In operation 325, process 300 generates a ranking of the plurality ofcohorts based on the score generated for each cohort of the plurality ofcohorts. In an embodiment, the cross-cohort optimization model scoringservice 115 ranks each of the cohorts according to their respectivescores. The scores may be ranked in descending order, where the highestscoring cohort is assigned the first position. the second highestscoring cohort is assigned the second position, and so on. Referring toFIG. 3, cohorts 300-330 are ranked in descending order, such that cohortPYMK-A 200 has the highest score and is ranked first. Cohort PYMK-B 210has the second highest score and is ranked second. Cohort hashtag 220has the third highest score and is ranked third. Cohort series 230 hasthe fourth highest score and is ranked fourth.

In operation 330, process 300 causes the plurality of content items ofeach cohort of the plurality of cohorts to be displayed concurrently,based on the ranking, on a computing device. In an embodiment,cross-cohort optimization system 105 sends the plurality of cohorts,including their respective rankings and their respective content items,to the content delivery system 120. The content delivery system 120 thenuses the publisher system 140 to cause display of the plurality ofcontent items on client device 142, where the plurality of content itemsis organized and displayed within their respective cohorts. Theplurality of cohorts is displayed in ranked order, with the highestranked cohort displayed at the top of the list of cohorts. In anembodiment, each of the cohorts, including their respective contentitems, are displayed concurrently on client device 142.

Cross-Cohort Optimization Model Training

The cross-cohort optimization model generation service 110 may train thecross-cohort optimization model for users using historical interactiondata from a subset of users. FIG. 4 depicts an example flowchart fortraining the cross-cohort optimization model by determining regressioncoefficients and latent intent values. Process 400 may be performed by asingle program or multiple programs. The operations of the process asshown in FIG. 4 may be implemented using processor-executableinstructions that are stored in computer memory. For purposes ofproviding a clear example, the operations of FIG. 4 are described asperformed by the cross-cohort optimization system 105 and itscomponents. For the purposes of clarity process 400 is described interms of a single entity.

In operation 405, process 400 retrieves historical user data for asubset of users that were presented with cohorts that were randomlyranked. In an embodiment, the cross-cohort optimization system 105 mayhave previously performed one or more online experiments on a subset ofusers within the content management platform. The subset of users mayrepresent a percentage of the total set of users on the contentmanagement platform. For example, the subset of users may be 5% of thetotal set of users. In other examples, the subset of users may be alarger or smaller percentage of the total set of users. The one or moreonline experiments may include presenting content items, within theirrespective cohorts, to the subset of users, where the cohorts arerandomly ranked. For example, ranking of cohorts is not determined byuser intentions, rather the randomly ranked cohorts are presented to thesubset of users and the subsequent user interactions are recorded. Thesubsequent user interactions and user profile attributes for the subsetof users represent the historical user data, which may be used to trainthe cross-cohort optimization model.

In an embodiment, the historical user data for the subset of users maybe formatted and stored within the data store 130 as training data. Thetraining data may include data and label data. The data may specify theranked order of cohorts displayed to users, subsequent user interactionswith content items within the ranked cohorts, user profile attributesfor each user that interacted with the content items within the rankedcohorts, and attributes associated with each of the cohorts presented.The label data may include metrics corresponding to values, such as a 1representing that the user clicked on one of the top three content itemswithin a ranked cohort and a 0 representing that the user did not clickon one of the top three content items within a ranked cohort. Thecross-cohort optimization system 105 may retrieve the historical userdata from the data store 130 for the purposes of training thecross-cohort optimization model.

In operation 410, process 400 uses historical user data from users toestimate regression coefficients for the machine-learning model withoutlatent intents. In an embodiment, the cross-cohort optimization modelgeneration service 110 may implement a cross-cohort optimization modelthat does not incorporate latent intent factors. Instead factorsassociated within latent intents of users are incorporated into an errorterm. The modified cross-cohort optimization model algorithm may berepresented as:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {\gamma P_{ij}^{FPR}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}} + ɛ_{ij}^{\prime}}$

where:ε′_(ij) represents an error term that incorporates both an error and anyobserved latent intent factors. The β, α₁, α₂, γ, δ_(k) representunknown logistic regression coefficients that are estimated as{circumflex over (β)},

,

, {circumflex over (γ)},

.

In operation 415, process 400 determines latent intent values using asecond optimization model and the estimated regression coefficients. Inan embodiment, the cross-cohort optimization model generation service110 may implement the second optimization model as a matrixfactorization model to estimate the latent intent values l_(i)^(T)c_(j). Matrix factorization is a class of collaborative filteringalgorithms that may be used to identify latent intent values. Forexample, matrix-factorization learns the latent factors by decomposingthe l_(i) ^(T)c_(j) values into the product of two vectors.Matrix-factorization learns vectors [l_(i1), l_(i2), l_(i3)]_(1×3) and[c_(j1), c_(j2), c_(j3)]_(1×3), the product of which yields l_(i)^(T)c_(j).

The following algorithm representing ε′_(ij) describes how the latentintent values l_(i) ^(T)c_(j) may be determined using matrixfactorization as:

ε′_(ij) =l _(i) ^(T) c _(j)+ε_(ij)

where ε′_(ij) includes the latent intent values l_(i) ^(T)c_(j) and theε_(ij) error term. Determining the latent intent values may be rewrittenas:

${{{\overset{\hat{}}{l}}_{i}^{T}{\overset{\hat{}}{c}}_{j}} + ɛ_{ij}} = {{{logit}\left( y_{ij} \right)} - \left( {{\overset{\hat{}}{\beta}x_{ij}} + {s_{i,{connect}}} + {s_{i,{{conte}nt}}} + {\overset{\hat{}}{\gamma}P_{ij}^{FPR}} + {\sum\limits_{k}{1_{\{{{{Type}\mspace{11mu} {of}\; j}==k}\}}}}} \right)}$

where {circumflex over (l)}_(i) ^(T) and ĉ_(j) represent estimatedlatent intent values, such that {circumflex over (l)}_(i)^(T)=[{circumflex over (l)}_(i1), {circumflex over (l)}_(i2),{circumflex over (l)}_(i3)]_(1×3) and ĉ_(j)=[ĉ_(j1), ĉ_(j2),ĉ_(j3)]_(1×3).

In operation 420, process 400 re-estimates regression coefficients forthe machine-learning model using the estimated latent intent values andhistorical data from users. In an embodiment, the cross-cohortoptimization model generation service 110 may use the estimated latentintent values from operation 415 and the historical data from users astraining data to re-estimate the regression coefficients. Byre-estimating the regression coefficients, accounting for the estimatedlatent intent values, the regression coefficients may be refined for thepurposes of training a more accurate cross-cohort optimization modelthat accurately accounts for the latent factors. The cross-cohortoptimization model used to re-estimate the regression coefficients maybe represented as:

${{logit}\left( y_{ij} \right)} = {{\beta \; x_{ij}} + {\alpha_{1}s_{i,{connect}}} + {\alpha_{2}s_{i,{{conte}nt}}} + {l_{i}^{T}c_{j}} + {\gamma P_{ij}^{FPR}} + {\sum\limits_{k}{\delta_{k}1_{\{{{{Type}\mspace{11mu} {of}\; j}\;==k}\}}}} + ɛ_{ij}}$

where the β, α₁, α₂, γ, δ_(k) regression coefficients are estimated asβ,

,

, γ,

.

In operation 425, process 400 determines whether the differences betweenthe estimated regression coefficients and the re-estimated regressioncoefficients are below thresholds. In an embodiment, the cross-cohortoptimization model generation service 110 may implement a set ofthreshold values that represent allowable differences between theestimated regression coefficients from operation 410 and there-estimated regression coefficients from operation 420. If thedifferences are below the set of thresholds, then that means there-estimated regression coefficients are similar enough to the estimatedregression coefficients such that the latent intent values l_(i)^(T)c_(j) have been accurately modeled and process 400 may proceed tooperation 430. If, however, the differences between the estimatedregression coefficients and the re-estimated regression coefficients aregreater than the set of thresholds, then the regression coefficients andthe latent intent values need to be recalibrated such that latent intentvalues are accurately reflected in l_(i) ^(T)c_(j) and the regressioncoefficients are adjusted accordingly.

In an embodiment, recalibration may include re-estimating the latentintent values using the re-estimated regression coefficients fromoperation 420. For example, process 400 may return to operation 415 tore-estimate the latent intent values using the matrix factorizationmodel and the re-estimated regression coefficients from operation 420 asthe regression coefficients. Upon re-estimating the latent intent valuesusing the matrix factorization model, process 400 may proceed tooperation 420 to re-estimate the regression coefficients using thecross-cohort optimization model, where the latent intent values are there-estimated latent intent values from operation 415. Process 400 maythen proceed to operation 425 to determine whether the differencesbetween the re-estimated regression coefficients and the newlyre-estimated regression coefficients are below the set of thresholds. Ifthe re-estimated regression coefficients and the newly re-estimatedregression coefficients are below set of thresholds then process 400 mayproceed to operation 430. If, however, the differences are still abovethe set of thresholds, then process 400 may return to operations 415 and420 to re-calibrate the regression coefficients and the latent intentvalues. Process 400 may iterate over operations 415-425 until thedifferences are below the set of thresholds.

In operation 430, process 400 stores the regression coefficients and thelatent intent values. In an embodiment, the cross-cohort optimizationsystem 105 may store the regression coefficients and the latent intentvalues in the data store 105 for later use by the cross-cohortoptimization scoring service 115.

Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 5 is a block diagram that illustrates a computersystem 500 upon which an embodiment of the invention may be implemented.Computer system 500 includes a bus 502 or other communication mechanismfor communicating information, and a hardware processor 504 coupled withbus 502 for processing information. Hardware processor 504 may be, forexample, a general purpose microprocessor.

Computer system 500 also includes a main memory 506, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 502for storing information and instructions to be executed by processor504. Main memory 506 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 504. Such instructions, when stored innon-transitory storage media accessible to processor 504, rendercomputer system 500 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 500 further includes a read only memory (ROM) 508 orother static storage device coupled to bus 502 for storing staticinformation and instructions for processor 504. A storage device 510,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 502 for storing information and instructions.

Computer system 500 may be coupled via bus 502 to a display 512, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 514, including alphanumeric and other keys, is coupledto bus 502 for communicating information and command selections toprocessor 504. Another type of user input device is cursor control 516,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 504 and forcontrolling cursor movement on display 512. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 500 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 500 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 500 in response to processor 504 executing one or more sequencesof one or more instructions contained in main memory 506. Suchinstructions may be read into main memory 506 from another storagemedium, such as storage device 510. Execution of the sequences ofinstructions contained in main memory 506 causes processor 504 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 510. Volatile media includes dynamic memory, such asmain memory 506. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 502. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 504 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 500 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 502. Bus 502 carries the data tomain memory 506, from which processor 504 retrieves and executes theinstructions. The instructions received by main memory 506 mayoptionally be stored on storage device 510 either before or afterexecution by processor 504.

Computer system 500 also includes a communication interface 518 coupledto bus 502. Communication interface 518 provides a two-way datacommunication coupling to a network link 520 that is connected to alocal network 522. For example, communication interface 518 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 518 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 518sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 520 typically provides data communication through one ormore networks to other data devices. For example, network link 520 mayprovide a connection through local network 522 to a host computer 524 orto data equipment operated by an Internet Service Provider (ISP) 526.ISP 526 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 528. Local network 522 and Internet 528 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 520and through communication interface 518, which carry the digital data toand from computer system 500, are example forms of transmission media.

Computer system 500 can send messages and receive data, includingprogram code, through the network(s), network link 520 and communicationinterface 518. In the Internet example, a server 530 might transmit arequested code for an application program through Internet 528, ISP 526,local network 522 and communication interface 518.

The received code may be executed by processor 504 as it is received,and/or stored in storage device 510, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A computer-implemented method comprising: storingcohort definition criteria for each cohort of a plurality of cohorts;for a particular user and for each cohort of the plurality of cohorts:identifying a plurality of content items based on the cohort definitioncriteria of said each cohort; using a machine-learned model to generatea score of said each cohort; based on the score generated for eachcohort of the plurality of cohorts, generating a ranking of theplurality of cohorts; based on the ranking, causing the plurality ofcontent items of each cohort of the plurality of cohorts to be displayedconcurrently on a computing device of the particular user; wherein themethod is performed by one or more computing devices.
 2. Thecomputer-implemented method of claim 1, wherein using themachine-learned model to generate the score of said each cohortcomprises calculating the score of said cohort with respect to theparticular user based upon at least one of user features of theparticular user, cohort features of said cohort, cross user-cohortfeatures of the particular user and said cohort, defined user intentsthat represent identified user intentions for a user session, or latentintents.
 3. The computer-implemented method of claim 2, wherein thelatent intents represent hidden intentions of a plurality of users basedupon historical user interaction data related to interacting with thecontent items within the plurality of cohorts.
 4. Thecomputer-implemented method of claim 2, further comprising: training themachine-learned model by: using historical user interaction data thatincludes interactions of a subset of users in response to beingpresented with content items within the plurality of cohorts displayedconcurrently in a randomly ranked order to derive estimated regressioncoefficients for the machine-learned model; determining latent intentsusing a second model and the estimated regression coefficients;re-estimating the regression coefficients using the machine-learningmodel and the latent intents; determining that differences between theestimated regression coefficients and the re-estimated regressioncoefficients are below thresholds; and storing the re-estimatedregression coefficients and the latent intents in memory for later usein the machine-learned model.
 5. The computer-implemented method ofclaim 2, wherein the user features of the particular user comprise atleast one of user profile attributes, historical user interaction data,a number of user invitations received by the particular user, a numberof connections for the particular user, or user interactions related tojob seeking.
 6. The computer-implemented method of claim 2, wherein thecohort feature of said cohort comprise at least one of a cohort type,content item-based rankings for content items within said cohort, orrandom-effect coefficients of said cohort.
 7. The computer-implementedmethod of claim 2, wherein the user-cohort features of the particularuser and said cohort comprise at least one of a said cohort-specificimpression metrics, and degrees of relations between the particular userand user profile-based content items within said cohort.
 8. Thecomputer-implemented method of claim 1, wherein the plurality of cohortscomprise one or more cohort types comprising a people-you-may-knowcohort type, a hashtag cohort type, or a series cohort type.
 9. Thecomputer-implemented method of claim 8, wherein the people-you-may-knowcohort type comprises at least one of a people-you-may-know that have asimilar job title, people-you-may-know went to the same school,people-you-may-know that work at the same company, andpeople-you-may-know that are in your industry.
 10. Thecomputer-implemented method of claim 1, wherein the machine-learnedmodel incorporates calculated content item based scores that representone or more desired objectives, wherein the one or more desiredobjectives comprise increasing click-through rate, increasing a numberof connections, increasing a number of hashtags followed, or increasinga number of subscriptions.
 11. The computer-implemented method of claim1, wherein the machine-learned model incorporates an affinity score foran observable user intent of increasing a number of connections for theparticular user.
 12. The computer-implemented method of claim 1, whereinthe machine-learned model incorporates an affinity score for anobservable user intent of increasing an amount of content items consumedby the particular user.
 13. A computer program product comprising: oneor more non-transitory computer-readable storage media comprisinginstructions which, when executed by one or more processors, cause:storing cohort definition criteria for each cohort of a plurality ofcohorts; for a particular user and for each cohort of the plurality ofcohorts: identifying a plurality of content items based on the cohortdefinition criteria of said each cohort; using a machine-learned modelto generate a score of said each cohort; based on the score generatedfor each cohort of the plurality of cohorts, generating a ranking of theplurality of cohorts; based on the ranking, causing the plurality ofcontent items of each cohort of the plurality of cohorts to be displayedconcurrently on a computing device of the particular user.
 14. Themethod of computer program product 13, wherein using the machine-learnedmodel to generate the score of said each cohort comprises calculatingthe score of said cohort with respect to the particular user based uponat least one of user features of the particular user, cohort features ofsaid cohort, cross user-cohort features of the particular user and saidcohort, defined user intents that represent identified user intentionsfor a user session, or latent intents.
 15. The computer program productof claim 14, wherein the latent intents represent hidden intentions of aplurality of users based upon historical user interaction data relatedto interacting with the content items within the plurality of cohorts.16. The computer program product of claim 14, wherein the one or morenon-transitory computer-readable storage media comprises furtherinstructions which, when executed by the one or more processors, cause:training the machine-learned model by: using historical user interactiondata that includes interactions of a subset of users in response tobeing presented with content items within the plurality of cohortsdisplayed concurrently in a randomly ranked order to derive estimatedregression coefficients for the machine-learned model; determininglatent intents using a second model and the estimated regressioncoefficients; re-estimating the regression coefficients using themachine-learning model and the latent intents; determining thatdifferences between the estimated regression coefficients and there-estimated regression coefficients are below thresholds; and storingthe re-estimated regression coefficients and the latent intents inmemory for later use in the machine-learned model.
 17. The computerprogram product of claim 14, wherein the user features of the particularuser comprise at least one of user profile attributes, historical userinteraction data, a number of user invitations received by theparticular user, a number of connections for the particular user, oruser interactions related to job seeking.
 18. The computer programproduct of claim 14, wherein the cohort feature of said cohort compriseat least one of a cohort type, content item-based rankings for contentitems within said cohort, or random-effect coefficients of said cohort.19. The computer program product of claim 14, wherein the user-cohortfeatures of the particular user and said cohort comprise at least one ofa said cohort-specific impression metrics, and degrees of relationsbetween the particular user and user profile-based content items withinsaid cohort.
 20. The computer program product of claim 13, wherein theplurality of cohorts comprise one or more cohort types comprising apeople-you-may-know cohort type, a hashtag cohort type, or a seriescohort type.